The Gaussian splatting methods are getting popular. However, their loss function only contains the $\ell_1$ norm and the structural similarity between the rendered and input images, without considering the edges in these images. It is well-known that the edges in an image provide important information. Therefore, in this paper, we propose an Edge Guided Gaussian Splatting (EGGS) method that leverages the edges in the input images. More specifically, we give the edge region a higher weight than the flat region. With such edge guidance, the resulting Gaussian particles focus more on the edges instead of the flat regions. Moreover, such edge guidance does not crease the computation cost during the training and rendering stage. The experiments confirm that such simple edge-weighted loss function indeed improves about $1\sim2$ dB on several difference data sets. With simply plugging in the edge guidance, the proposed method can improve all Gaussian splatting methods in different scenarios, such as human head modeling, building 3D reconstruction, etc.

翻译：高斯溅射方法正逐渐流行。然而，其损失函数仅包含渲染图像与输入图像之间的$\ell_1$范数和结构相似性，未考虑图像中的边缘信息。众所周知，图像边缘蕴含着重要信息。因此，本文提出一种基于边缘引导的高斯溅射（EGGS）方法，充分利用输入图像中的边缘特征。具体而言，我们赋予边缘区域比平坦区域更高的权重。在这种边缘引导机制下，生成的高斯粒子更聚焦于边缘而非平坦区域。此外，这种边缘引导不会增加训练与渲染阶段的计算成本。实验表明，这种简单的边缘加权损失函数在不同数据集上确实能提升约$1\sim2$dB的性能。通过简单引入边缘引导机制，本文方法可改进不同场景下所有高斯溅射方法的表现，例如人体头部建模、建筑三维重建等。